We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone.
Recent articles
- Trying out the new Gemini 2.5 model family - 17th June 2025
- The lethal trifecta for AI agents: private data, untrusted content, and external communication - 16th June 2025
- An Introduction to Google’s Approach to AI Agent Security - 15th June 2025